Kelp-Sea Urchin-Fishermen: a Spatially Explicit Individual-Based Model Nicolas Gutierrez and Ray Hilborn School of Aquatic and Fishery Sciences University of Washington July 12 th 2006 Flow, Fish, Fishing Meeting

Sedentary species, planktonic larvae Metapopulation structure Omnivorous grazers Small scale variations in life history traits Broadcast spawners with variable seasons Photo:: The red sea urchin Strongylocentrotus franciscanus

Photo: Sea Urchin Harvesters Association website Status Harvestable stocks (individuals > legal size and containing marketable gonads) in decline since 1990 Fully exploited, and some evidence of overfished conditions Management Scheme Limited entry (385 permits) Fishing restricted to 240 days Minimum size limit = 89 mm The Southern California Fishery

Tight ecological coupling Roe fishery Broadcast spawners Protection of juveniles under adult spine canopies Special features Photo:

Objective Integrate all ecological, biological, and fishery information in a simple but realistic spatially-explicit individual-based model Reproduce the temporal and spatial dynamics of sea urchins, its resource (kelp) and its fishery

Why a spatially explicit IBM ? In IBM’s, each individual or agent in a population is simulated through its lifetime on a spatially explicit habitat The relevant empirical life cycle, physiological and behavioral data that are available can be included in such models The effects of behavior and of fine resolution environmental variability can be taken into consideration Small scale stocks structure (microstocks) can be detected and spatial management strategies assessed

The Model: Purpose Some hypotheses arise from the special features of sea urchin and its fishery: H2 Low densities cause both a decrease in the production of zygotes and a decrease in suitable settlement habitat and juvenile protection (adult canopies) resulting in lower recruitment levels and stock productivity H3 Fishing for “good quality roe” allows the persistence of source populations contributing to the harvestable stock H1 The tight ecological coupling between kelp and sea urchin alters its behavior, reproductive output and population persistence

The Model: Structure Agents Individual urchins (number, location, birth date, size, weight, energy, gonad index, food consumed) Individual boats/divers/trips Cell attributes Resource quality: Kelp biomass (updated every year) Bottom type (habitat cover: % of crevices) Fishery quality: Total roe content (updated every month)

The Model: Structure Grain and extent Spatial: 5 x 5 m cells; 1400 x 700 cells (~27 km 2 ) Temporal: Monthly (yearly); 50 years

The Model : Flowchart

The Model: Processes Sea Urchin Movement -Loop over Urchins -Get quality of adjacent cells: kelp biomass -Loop over neighbor cells -Draw a random number and choose where to go -Move and update new Location and Roe content of the cell Energetics -Loop over Urchins -Determine amount of food consumed and update food at each cell -Determine Total Energy consumed, Surplus Energy, Energy spent in grow and Energy spent in reproduction -Update Size, Weight and Gonad index

SEA URCHIN KELP METABOLIC COST GAMETES ENERGY SURPLUS ENERGY GONADS SOMATIC GROWTH GROW SUBSIST REPRODUCE REPRODUCE + GROWTH As a function of energy intake, sea urchin size and month of the year Sub Model: Sea urchin energetics

The Model: Processes Sea Urchin (cont) Survival -Loop over Urchins -Get probability of survive -Draw a random number and decide if alive or not -Update Urchin list Reproduction -Get number of Recruits as a function of Gonad index -Set conditions for new Recruits (Number, Location, Size, Weight, Energy, etc) -Update Urchin list

The Model: Processes Fishing -Loop over fishing sites (10 x 10 cells) -Find the fishery quality (based on roe content) -Loop over trips by month and find the Best Fishing Site -Harvest all legal urchins in the Best Site -Update Catch and Roe content per cell -Kill individuals and update Urchin list Kelp -Loop over cells and update Kelp dynamics (Schaefer surplus model with stochasticity) Photo: Ken Curtis

The fishery Scenarios

The fishery Summary Where we are… Literature reviewed Major hypotheses identified Model running Where we go… Data Gathering (Barefoot Ecologist; UCSB surveys) Tune and stabilize model Drive model with historical data (ascii files: kelp, bottom type) Run scenarios and test hypothesis Summarize and publish results

The fishery Acknowledgements Eric Ward, Gavin Fay and Bob Lessard Peter Halmay and Steve Schroeter Funding National Science Foundation: A Biocomplexity in the Environment Project Fulbright Program and OAS Initiative in Ecology Program